System and method for analyzing electroencephalogram signals

ABSTRACT

A system, a computer readable storage medium, and a method for analyzing electroencephalogram signals can include a plurality of sensors configured to contact a skull and capture the electroencephalogram signals, one or more computer memory units for storing computer instructions and data, and one or more processors configured to perform the operations of clustering the electroencephalogram signals using at least stored objective data and added subjective data including patient profile data to provide clustered data results and predicting one or more among a medical diagnosis, assessment, plan, necessary forms, or recommendations for follow up based on the clustered data results.

BACKGROUND

The present disclosure generally relates to a computer system andmethod, and more particularly relates to a system and method foranalyzing electroencephalogram (EEG) signals using a plurality ofsensors.

Existing analysis of EEG signals primarily relies on recognition byindividual experts and fails to take advantage of all the automationtools that might be incorporated in forming a useful analysis tool. Acompendium of historical data that might supplement such expertise isnot used in any formal fashion and normalization of such existing datato enable useful rendering of such supplementation has not beenpreviously contemplated.

Existing methods to improve or verify data and further interpret suchdata suffer from poor efficiency or accuracy. A manual review of thedata is very inefficient and could take quite a long time. Statisticalmethods can use simple thresholds, but suffer from poor accuracy and cancause deviant interpretations without appropriate analysis.

SUMMARY

According to one embodiment of the present disclosure, a method for ofanalyzing electroencephalogram signals can include capturing theelectroencephalogram signals using a plurality of sensors configured tocontact a skull, clustering the electroencephalogram signals using atleast stored objective data and subjective data including patientprofile data to provide clustered data results, and predicting one ormore among a medical plan based on current practice parameters for thefield of psychiatry or a diagnosis based on the clustered data results.

According to another embodiment of the present disclosure, a system foranalyzing electroencephalogram signals includes a plurality of sensorsconfigured to contact a skull and capture the electroencephalogramsignals, one or more computer memory units for storing computerinstructions and data, and one or more processors operatively coupled tothe one or more computer memory units and the plurality of sensors. Theone or more processors can be configured to perform the operations ofclustering the electroencephalogram signals using at least storedobjective data and added subjective data including patient profile datato provide clustered data results and predicting one or more among amedical diagnosis, assessment, plan, necessary forms, or recommendationsfor follow up based on the clustered data results.

According to yet another embodiment of the present disclosure, anon-transitory computer readable storage medium can include computerinstructions which, responsive to being executed by one or moreprocessors, cause the processor(s) to perform operations as described inthe methods or systems above or elsewhere herein.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying figures, in which like reference numerals refer toidentical or functionally similar elements throughout the separateviews, and which together with the detailed description below areincorporated in and form part of the specification, serve to furtherillustrate various embodiments and to explain various principles andadvantages all in accordance with the present disclosure, in which:

FIG. 1 is a depiction of flow diagram of a system or method foranalyzing electroencephalogram signals using a plurality of sensorsaccording to various embodiments of the present disclosure;

FIG. 2 is another depiction of flow diagram of a system or method foranalyzing electroencephalogram signals using a plurality of sensorsaccording to various embodiments of the present disclosure;

FIG. 3 is depiction of flow diagram of a portion of the system or methodfor analyzing electroencephalogram signals that performs a narrowed downprediction according to various embodiments of the present disclosure;

FIG. 4 is depiction of flow diagram of a portion of the system or methodfor analyzing electroencephalogram signals that performs a broad orunguided prediction according to various embodiments of the presentdisclosure;

FIG. 5 is depiction of flow diagram of a portion of the system or methodfor analyzing electroencephalogram signals that performs a guidedprediction according to various embodiments of the present disclosure;

FIG. 6 is depiction of flow diagram of a portion of the system or methodfor analyzing electroencephalogram signals that performs a flatprediction according to various embodiments of the present disclosure;

FIG. 7 is a depiction of a user interface for broad or unguidedprediction according to various embodiments of the present disclosure;

FIG. 8 is a depiction of a user interface for a guided predictionaccording to various embodiments of the present disclosure;

FIG. 9 is a depiction of a user interface for a narrowed down predictionaccording to various embodiments of the present disclosure;

FIG. 10 is a depiction of a user interface for a flat predictionaccording to various embodiments of the present disclosure;

FIG. 11 is a depiction of a user interface for correcting and/orverifying a prediction by or from experts according to variousembodiments of the present disclosure;

FIG. 12 is a depiction of a user interface used for prediction mappingaccording to various embodiments of the present disclosure;

FIG. 13 is a depiction of a user interface for annotating or loggingreasons for the predictions according to various embodiments of thepresent disclosure;

FIGS. 14A, 14C, 14D, and 14E depict illustrations of various electrodearrays and FIG. 14B is an illustration of a form of electrode shown infurther detail that is used in at least one of the plurality of sensorsthat captures the electroencephalogram signals according to variousembodiments of the present disclosure;

FIG. 15 is an illustration of a tape or sensor array optionally used inconjunction with the systems and methods according to variousembodiments of the present disclosure;

FIG. 16 is a block diagram of a system according to various embodimentsof the present disclosure;

FIG. 17A, FIG. 17B, and FIG. 18 are block diagrams of a portion of asystem according to various embodiments of the present disclosure;

FIG. 19 is a block diagram of a system according to various embodimentsof the present disclosure; and

FIG. 20 is an illustration of a wave band cluster in accordance withvarious embodiments of the present disclosure.

DETAILED DESCRIPTION

As required, detailed embodiments of the present disclosure aredisclosed herein; however, it is to be understood that the disclosedembodiments are merely examples of the disclosure, which can be embodiedin various forms. Therefore, specific structural and functional detailsdisclosed herein are not to be interpreted as limiting, but merely as abasis for the claims and as a representative basis for teaching oneskilled in the art to variously employ the present disclosure invirtually any appropriately detailed structure and function. Further,the terms and phrases used herein are not intended to be limiting; butrather, to provide an understandable description of the disclosure.

According to various embodiments of the present disclosure, disclosed isa system and method for analyzing electroencephalogram signals using oneor more sensors and more typically using a group of two or more sensorsto enhance results beyond the results provided by existing monitoringsystems. The embodiments utilize advanced neural network analysis (ANNA)to improve the accuracy and predictive analysis of brain related data toenable not only a monitoring tool, but also a clinical tool enablingrapid psychiatric diagnosis and treatment recommendation. Thus, a cloudbased, machine learning, standardized diagnostic tool can increase,according to the embodiments, the timeliness and accuracy of brainrelated data for physicians and healthcare providers. Such tool utilizesANNA to discover new patterns and associate them with certain ailmentsor treatments, and to make diagnostic predictions and risk assessments,to determine a necessity for psychiatric admissions. In someembodiments, ANNA can use specific triggers for outcome analysis. Forexample, certain aromas, sounds, or lighting can trigger particularexpected outcomes or event related potentials.

ANNA (“Advanced Neural Network Analysis”) is a clinical tool to assistin Quantitative EEG analysis, rapid psychiatric diagnosis, and treatmentrecommendations. ANNA can use a proprietary algorithm to analyze thedeconstructed Mortlet Wavelet portion of an electroencephalogram (EEG)signal via a variation of K-Means Cluster Analysis. The descriptionunderlying the development and technical flow of ANNA will revealseveral potential applications for this diagnostic tool.

ANNA's datasets include thousands of patients' Electroencephalograms(referred to as “objective data”), subjective data from the clinicianand subject (or current patient), and functional data in the form ofonline performance testing. Referring to the flow chart of FIG. 1, amethod 10 can begin by capturing electroencephalogram or EEG signalsusing a plurality of sensors configured to contact the skull at step 11.The process progress at step 12 by decoding raw data into clusteringalgorithms (algorithms used for machine learning). In other words, themethod clusters the EEG signals using stored objective data and storedsubjective data including patient profile data to provide clustered dataresults. The method can optionally perform (when necessary) datatransformation of the EEG signals to enable a comparison of normalizedEEG signals with stored objective data at step 13. Once the datatransformation and clustering phase is done what follows is a predictionstep 14 where a prediction of a medical plan and/or diagnosis isprovided based on the clustered data results. Such prediction step caninclude global clustering (specific to ANNA) using various clusteringalgorithms, and prediction techniques based on the data patterns anddecision maps ANNA learned so far from previous patients' analysis. Suchprediction techniques can use any one among an unguided prediction, aguided prediction, a narrowed down prediction, or a flat prediction. Atstep 15, the method can present options for a user of ANNA forpredicting the diagnosis using one (or more) of the aforesaid predictiontechniques. At step 16, the method 10 can further enable the user (as aclinical expert) to correct or verify the predicted diagnosis presentedby ANNA.

In clustering and prediction phase (steps 13 and 14) ANNA can take intoaccount patient's personal details like age, gender, handedness,history, and pertinent medical issues. During the prediction phase, ANNAcan collaborate with the clinician to come to a rational diagnosis andplan. One can simply define ANNA as a Supervised Machine LearningAssistant in EEG analysis. In general, the technical flow of ANNA'soperations track the method 10 of the flow chart of FIG. 1 whichincludes data gathering or capturing, data transformation, clustering,global clustering, predicting, and correcting and/or verifyingpredictions from experts.

Referring to FIG. 2, another flow chart illustrates a method 20 inaccordance with the embodiments in yet further detail. At step 21, themethod gathers data including individual data, subjective data and EDFraw data files. Accordingly, a user can enter a patient's personaldetails like age, gender and subjective information, medication, anyspecific complaints, and some facts that might matter to EEG analysis.This information is later used in data clustering and in linking to theEEG data.

At step 22, data transformation includes decoding raw data and/ortransformation of subjective data. At step 23, additional informationfrom an expert about the patient in the form of questions and answerscan be stored in a database and made available for the datatransformation. Similarly, individual data and subjective data, whichcan include complaints from patients, can also be stored at step 24 in adatabase and made available for use for the data transformation step aswell as for a subsequent prediction step (26B) via step 26A. Next, insome embodiments, the method 20 can include the step 25A of clusteringover decoded results and transformed subjective data, which can befollowed by step 25B of saving patterns of data in a database, andindexed in a way that could be readable or retrieved in a rapid manner.Method 20 can also perform global clustering at step 25C. At step 26,the method can make predictions based on comparison analysis made usingdifferent predictions methods. Some or all of the individual data (suchas gender and age information) obtained at step 24 can be passeddirectly and utilized by the prediction step 26. Optionally, the method20 can include the verification and/or correction stage 27 where expertinformation is used to verifying or correct predictions made by ANNA.Furthermore, as an iterative improvement process, learned predictionmaps can be stored by ANNA for future use at step 28.

The data transformation 22 can include decoding raw data using severaldifferent alternative analysis methods such as Absolute power analysis,Relative power analysis, Amplitude Asymmetry connectivity analysis,Coherence connectivity analysis, Phase Lag analysis, Phase Shiftanalysis, Phase Lock analysis, current source density analysis, evokedpotential analysis, or Low Resolution Electrographic Analysis.

Decoding raw data for “Absolute power analysis” can involve extractingAbsolute power data for each Frequency by performing FFT (FastestFourier Transform) between the raw data (complex sine waves in theory)and a complex morlet wavelet sine wave for each frequency listed. A FFTis calculated using a dot product between both complex sine wavesreferenced above.

The background FFT and IFFT (inverse FFT) is performed to get back tothe time domain from frequency domain and then power is averaged overthe trial for each channels/montages.

Theta (4-8 Hz), beta (13-25 Hz), and gamma (80-120 Hz) are exemplaryfrequency ranges or bands that are analyzed for EEG signals. In acurrent embodiment, ANNA will have decoded results in the form of 5vectors (a vector for each frequency band) which can be increased to 30vectors or more with the number of dimensions equal to a number ofchannels/montages in raw data. In short, each element/dimension in thevector represents absolute power value for a particular channel/montage.Results displayed in the form of a vector with each channel/montagerepresent an element/dimension in the vector analysis. ANNA isprocessing data up to the channel/montages level, and data beyond thatdepends on the sampling rate and a number of trials (e.g., there are17664 data points for a sampling rate of 256 with 69 trials of 1 secondeach) that are averaged. In the future, with additional processingpower, ANNA can perform 3D analysis, and functional network analysisusing a novel inverse solution to render the voxels necessary. A voxelrepresents a value on a regular grid in three-dimensional space. As withpixels in a bitmap, voxels themselves do not typically have theirposition (their coordinates) explicitly encoded along with their values.Instead, rendering systems infer the position of a voxel based upon itsposition relative to other voxels.

Decoding raw data for “Relative power analysis” involves a differentprocess. After the absolute power extraction, what follows for relativepower extraction is normalizing data for extracting relative dataresults. It is performed using log-based normalization using in theorythe decibel method for normalizing data. To perform normalizationagainst some base or relative parameters, a time window is selected fromthe range of raw data recording time. The resulting format is same as inAbsolute power analysis.

Decoding raw data for “Amplitude Asymmetry connectivity analysis”involves decoding methods for connectivity results that are muchdifferent than the two analyses above, but results are not currently asaccurate in implementation in comparison to other EEG tool results.Further refinements are needed. The connectivity result format for“Amplitude Asymmetry connectivity analysis” is much different than thetwo analysis above, but will operate more robustly after subjective dataclustering is implemented.

Decoding raw data for “Coherence connectivity analysis” also usestechniques that are much different than the analysis used for AbsolutePower Analysis or Relative Power Analysis. Although such techniques canbe implemented with ANNA, results are not currently as accurate inimplementation in comparison to other EEG tool results. After subjectivedata clustering is implemented, better results are anticipated.

Decoding subjective data can be straight forward to enable furtherprocessing such as clustering of such data.

Clustering is a field of machine learning technology used widely in manymachine learning applications in data mining in a field of computerscience. ANNA is mainly and specifically using a K-Means algorithm (oneof the popular or common clustering algorithms out of many knownclustering algorithms in computer science). There are few differentflows (or sub areas) of implementing K-Means as per the requirement ofdata analysis. The K-Means algorithm in general enables ANNA to utilizedata from the results of raw data decoding explained above and then passthe processed data to a clustering module and come up with clusters ofdata that will serve the purpose of identifying similarity patterns andeventually help in making predictions.

ANNA can utilize various clustering techniques in alternativeembodiments, but the embodiments described herein use K-Meansclustering. A K-Means algorithm in non-technical terms, is considered aproved theory of computer science which enables computers to classifyinformation in the form of information clouds”. K stands for a number ofclusters, where the algorithm is provided with n observations and thealgorithm classifies them in K number of clusters. Deciding a value forK depends on the type of data analysis requirement for a particularapplication and there are methods for deciding K dynamically also.

Decoded Data Clustering entails how the results from decoded data aremapped in clusters and what phases it goes through for making it usablefor global clustering and flat predictions (or other predictiontechniques if desired).

As explained above regarding “result format” in the decoding sectionabove, the decoded results are available for each frequency bands at thelevel of channels/montages. Clustering is performed on these availableresults for each analysis methods using K-Means clustering methodsexplained above. However, in some embodiments, ANNA does not use a plainimplementation of K-Means but there are some trial and error checkmechanisms used to find the best K. The K-Means algorithm can beimplemented in C# programming language and there can be one dependencylibrary used for that implementation available through “Accord.Net”.Note that the K-Means algorithm can also be implemented in otherprogramming languages such as Python. After the clustering phase isover, a chosen K cluster provides information such as “to which clusterparticular individual observation (decoded result) belongs” or “how thedifferent cluster represents different group of observations withsimilarities”. This information is saved in the ANNA database for lateruse in global clustering. It is important to note that the decodingphase is performed only on the individual readings for which analysis isperformed while the clustering and global clustering is performed overall the observations known to ANNA or in simple words available in theANNA database.

Subjective Data Clustering entails Subjective data processing to enablethe availability of such data to other portions of the method orprocess.

Global Clustering entails clustering on decoded data clustering results.As explained in decoded data clustering, information is now available informs that indicate how different observations are grouped together orto which group one particular patient observation belongs.

Clustering further entails statistical comparison and clustering of allresults of all different frequency bands clusters to enable the findbest matching patterns. The result of global clustering are patternssimilar to patient's reading/observation being analyzed by ANNA. Thesepatterns are useful in making predictions. Global Clustering onSubjective data clustering results can help further refine the processand enable more accurate predictions.

The prediction phase is the last phase of ANNA's analysis process and itis fairly simple. The prediction process takes matched patterns from theresults of previous phases, find patient's references from patterns andsimply makes predictions, which were made for those patients. Howeverthere are currently various prediction approaches implemented in aneffort to provide insight and focus on a prediction method which is moreefficient for future versions.

Predictions based on global cluster results can find a patient'sreferences from patterns and simply make predictions which were made forthose patients.

FIGS. 3-6 illustrate various methods using various prediction techniquesor methods in accordance with the embodiments. FIG. 3 illustrates anarrowed down predictive method using an absolute power analysis 31A, aclustering 31B over frequency bands (in one embodiment, over 19different channels, but any number can be used), and a clustering at 31Cbased on a match count. Similarly, a relative power analysis 32A, aclustering 32B over frequency bands (in one embodiment, over 19different channels, but any number can be used), and a clustering at 32Cbased on match count is done alternatively or simultaneously. Once theclustered results over the frequency bands are gathered, a determinationof whether to use guided results is made at decision block 33. If guidedresults are to be used, then guided clustering is done on match countcluster results where higher value or greater weight is given toanalysis results from the absolute power analysis for example at step 34before extracting at 35 the nearest individuals or patients in the matchcount clusters. If guided results are not used at decision block 33,then the method directly extracts the nearest individuals or patients inthe match count clusters at 35. At step 36, ANNA checks or confirms ifthe matched individual in the database and the individual being analyzedare in the same cluster due to the matching symptoms. The matchingsymptoms are from matched individual symptom histories and the clusteris further flagged or mapped with the new results through a “systemmapping tree”. At step 37, the list of symptoms matched through theanalysis above are presented or displayed in one embodiment as part of a“Narrowed Down” predictions tab. The graphical user interface 90 in FIG.9 provides a sample of a display of a “Narrowed Down” predictions tabproviding the symptom predictions of “Inattention” or

“Hyperfocused, Irritable”.

FIG. 4 illustrates a broad or unguided prediction method using anabsolute power analysis 41A or a relative power analysis 42A or both.With absolute power analysis 42A, the method further performs aclustering 41B over frequency bands, a clustering at 41C based on amatch count, an extraction 41D of the nearest individuals or patients inthe match count clusters, and a look up 41E in ANNA's database forsymptoms history of the found individuals. Similarly, with a relativepower analysis 42A, a clustering 42B over frequency bands (in oneembodiment, over 19 different channels, but any number can be used), aclustering at 42C based on match count is done alternatively orsimultaneously, an extraction 42D of the nearest individuals or patientsin the match count clusters, and a look up 42E in ANNA's database forsymptoms history of the found individuals. In this particular approach,the generation of a list of symptoms is unguided and presented at 43.The presentation can be a display of a Broad or Unguided Tab on agraphical user interface (GUI) 70 as illustrated in FIG. 7. In thisexample, the Unguided Tab of the GUI 70 displays the symptoms ofInattention, Hyperfocused-Irritable, Traumatic Brain Injury Mild, PoorSleep, and Visual Process.

In some embodiments, the Guided Prediction method as illustrated in themethod 14C of FIG. 5 includes a slightly different approach by takingsame matched patterns results from previous phases but instead ofchoosing predictions it gives weight to patterns which are the resultsof clusters of analysis methods which are more valuable or importantfrom the EEG experts point of view in making decisions after observingpatient's readings. More particularly, the method 14C performs anabsolute power analysis at 51A, clusters on frequency bands at 51B, andclusters on the match count at 51C. Method 14C simultaneously oralternatively performs a relative power analysis at 52A, clusters onfrequency bands at 52B, and clusters on the match count at 52C. Once theclustered results over the frequency bands are gathered, in this guidedprediction method then guided clustering is performed on match countcluster results where higher value or greater weight is given toanalysis results from the absolute power analysis for example at step 53before extracting at 54 the nearest individuals or patients in the matchcount clusters. For example normally EEG experts would give more weightto Absolute power results than relative power results. However it isconfigurable in ANNA, so a user of ANNA can decide what weight to giveto a particular analysis method. For example, the weighting can rangefrom 1 to 10. At step 55, ANNA checks or confirms if the matchedindividual(s) in the database and the individual being analyzed are inthe same cluster due to the matching symptoms. At step 56, the list ofsymptoms is presented such as on a Guided Predictions Tab of a GUI 80 ofFIG. 80. For example, the user interface 80 illustrates a guidedpredictions tab which displays the potential symptoms of “inattention,Hyperfocused, Irritable, Traumatic Brain Injury Mild, Poor Sleep, VisualProcessing, etc.”.

In some embodiments, the Narrowed Down Prediction method 14A asillustrated in FIG. 3 and discussed above can be considered a filteredversion of global predictions made, where techniques used for filteringare based on best match logic. In simple terms, the narrowed downprediction method just drops the predictions that are not best matchesand may not be as accurate as other prediction techniques depending onANNA's assumptions.

Referring to FIG. 6, a method 14D illustrates a flow chart where FlatPrediction analysis is used. The flat prediction method 14D can havequite a different flow of making predictions or a flow that is veryflat, hence the given name of “Flat Predictions”. This is a straightforward implementation of making predictions as part of the port processafter clustering while the above two methods were custom implementationsspecific to ANNA. While the “Flat Predictions” method is straightforward, it has disadvantages of potentially being noisy as the numberof observations grows in the ANNA database. More particularly, themethod 14D performs an absolute power analysis at 61A, clusters onfrequency bands at 61B, and clusters on the match count at 61C. Method14D simultaneously or alternatively performs a relative power analysisat 62A, clusters on frequency bands at 62B, and clusters on the matchcount at 62C. Once the clustered results over the frequency bands aregathered, a flat prediction is performed at 63 and a list of systemsbased on the flat prediction is presented or displayed at 65. Flatpredictions work by simply deriving patterns directly from decodedcluster results so it skips global clustering done in the other analysismethods. The comparison done by flat prediction analysis depends onmaking predictions from the same clusters from patient observationsusing the level of frequency bands signal. Thus, a user interface 92 asshown in FIG. 10 might display a Flat Prediction Tab showing a symptomof “Inattention” based on left frontal Theta wave between 4-7 Hz, asymptom of “mood instability” and “potential transcendental meditator”based on increased amplitude t5.O1 (left Temporal Lobe T5 and memoryencoding with semantic tasks with respect to the Occiptal Lobe O1) inthe 8-9 Hz range, and symptoms of “Hyperfocused” and “Irritable” basedupon increased 13-17 Hz Central Midline signaling. The other methods mayprovide similar results, but not always.

Correcting and/or verifying predictions from experts (as noted in step16 of FIG. 1 or step 27 of FIG. 2) is a very crucial phase from theperspective of verifying predictions from EEG experts as this is howANNA learns from experts in a form of Artificial Intelligence. In thisphase, the user is supposed to verify or correct the predictions ANNAmade, add predictions that ANNA possibly missed in the event ANNA'sdatabase failed to previously include sufficient learned information tomake such predictions yet. From a practical standpoint, the correctionor verification phase can be implemented in one or more graphical userinterfaces as shown in FIGS. 11-13. GUI 93 of FIG. 11 provides a listingof assumed symptoms for a particular observed individual or patient. TheGUI 93 can include a listing of assumed symptoms for a particularobserved individual where each of the symptoms can be selected orde-selected based on expert knowledge of the GUI user to correct orverifying the assumed symptoms shown. In some instances, the listedsymptom can include an option that acknowledges that a predicted symptomwas based on a particular theory or analysis such as a flat basedprediction such as based on “BETA issues in ABSOLUTE POWER vs. BANDS”.As shown further in GUI 94, if the expert wants to specify or change thebasis of the prediction, then there is an option in the form of a buttonto change such basis. For example, if the basis of the symptom of“Insomnia” was wrong or unspecified, the GUI enables the user to changethe basis. Referring further to the GUI 95 of FIG. 13, the GUI 95 caninclude a pull down menu, a selectable menu, or text entry field toprovide expert based symptoms. Note that data can also be entered intoto text entry field using other data input mechanisms and engines thatutilize speech to text entry or smart artificial intelligence enginessuch as IBM's Watson as an interface to the ANNA system. Although Watsonmay have certain data useful for the overall process, Watson will nothave specific data for brainwave analysis and patient assessment thatwill be specifically used herein. For example, pre-set selectableoptions for describing or setting the basis (or additional basis) of anassumed basis for a symptom such as “Pain” (from FIG. 12) could be basedon Absolute Power over bands or Relative Power over bands, or Amplitudeasymmetry over bands, or coherence over bands. The particular bands thatform the basis of such assumption can also be noted (Delta, Theta,Alpha, Beta, High Beta, etc.). If there is another basis that is notlisted as a pre-selected option, the GUI 95 enables the user to write ina basis using text in a text field.

Prediction mapping in ANNA for patients are stored in ANNA's databaseenable the system to improve its ability to deliver better and accurateresults. Prediction mapping is information that maps a patient's symptomwith the cause for that symptom which can be predictably associated withcertain signaling profiles known by experts. For example, if the patientis anxious, then the cause can be presumed to be high beta issues asmight be mapped in ANNA's database. Prediction mapping entries arestored prior to displaying such resultant predictions to a user andupdated after the corrections are made by experts so the system will beconsidered robust and in some embodiments always in a state ofimprovement and reliability. As the system is configured to frequentlyor always verify, correct and supply additional information(ifapplicable) in this phase, the prediction mapping in ANNA inherentlymakes better predictions for future patients. In some respects, ANNA isconsidered self-improving or self-healing due to this most importantinterface with experts where ANNA could take information from humans andlearn.

Extensions of the outputs that ANNA can provide are numerous. In someembodiments, the outputs that ANNA provides can include output reportsincluding information that an expert, physician or clinician would needto render the correct insurance billing codes, and subsequent steps tolegally maximize billing under a current regime or could employ aprotocol with the least amount of risk. The system can also compareprevious treatment success outcomes and make predictions regarding thechances of success of a given plan. The system can also automaticallyprogram an EEG-Biofeedback protocol into a user end of the system andrun this protocol on any device that has Java-script. Such device caninclude a desk top computer, a smart phone, lap top computer, or tabletas examples. In future embodiments, the device biofeedback protocol canbe incorporated into augmented reality or virtual reality devicescurrently or contemplated to be introduced into the market. The devicewill support the training aspect of the entire system and makerecommendations regarding the placement of electrodes or arrays ofelectrodes in cap form or in tape designs referred here as “Neurotape”.The placement of the electrodes in caps or neurotape or whatever formfactor can be based on previous outcomes, subjective data and storeddata to enable the user or practitioner to appropriately adjust theplacement of such electrodes on a skull or scalp. Aspects of Neurotapehave its own utility and independently improve specific issues of EEGCoherence without having to use Neurotape with ANNA. The usefulness ofNeurotape is only further magnified and made apparent with used inconjunction with ANNA. That this tape would be disposable and used asper directions of ANNAs system.

Although the use of ANNA is not limited for use with a particularimplementation of a plurality of electrodes, Neurotape will enhancesignaling and results. Other embodiments for a plurality of electrodesor sensors can include a fixed cap or a customized cap that can beprinted for each individual making sizing more consistent. Such aprinted cap can be made or flexible material or of more durable orhardened materials and is feasible today with current 3D printingtechniques. In some embodiments, the entire system can work on“NeuroJavascript” which will be cross platform. In some embodiments, thesystem can also include a feedback system based on dimming a user'sscreen and raising and lowering the volume of the device rather thensome additional piece of software that enables feedback. Thus, thefeedback mechanism can be simple and avoid additional hardware costs ifthe hardware already includes visual and/or audio outputs. Again,Neurotape can be used with or without ANNA. In some embodiments,Neurotape can be analogous to a customizable Breathe-right strip thatcorrects a condition detectable by EEG signaling. This tape can bemass-produced and the directions for each disorder printed on theinstructions. Once a condition is determined or suspected, the Neurotapecan be appropriately placed on the skull to help rectify or alleviate aparticular detected or suspected ailment or condition. Using ANNA, thepredicted ailment or condition and corresponding prescription to rectifysuch ailment or condition can be tailored making appropriate neuralconnections using Neurotape.

Referring to FIG. 14A, a sensor array 145 illustrates a plurality ofelectrodes 140 or sensor podia all coupling to a control hub 142 via aconnector leg 141 that includes an internal conductive connector betweenthe electrode 140 and the control hub 142. The array 145 can be placedon a users head and each electrode 140 can be placed appropriately atdesignated locations on the user's head. The electrode 140 is shown infurther detail in FIG. 14B and can be used in conjunction with ANNA.Although any conventional EEG electrode can be used with ANNA, betterresults can be provided using an electrode that will provide bettercontact with the scalp or skull and that further has inherentinterference immunity characteristics due to its structure andcomposition. In this regard, the electrode 140 can include a pluralityof prongs or microfilaments 144 that can bury through a thicket of hairto provide direct contact with the scalp or skull. The microfilaments144 can be encased or covered by a coating (such as graphene) to providea Faraday cage 146 to make the signaling obtained from themicrofilaments 144 more immune from external interference. Similarly theconnector leg 141 can be coated with graphene or a graphene composite.The leg 141 and Faraday cage 146 can be formed of an integrated grapheneor graphene composite body. More particularly, electrode 140 can be partof the plurality of sensors (145) that are electroencephalogram sensorshaving prongs extending less than a particular distance from the centralhub 142 at equidistant angles from one another and forming podiaextending outwards at an angle from the shaft and in some embodimentsfurther formed of graphene. In some embodiments, the EEG sensors caneach have prongs extending less than 2 centimeters from the central hub142. In some embodiments, the EEG sensors (145) can each have prongsforming podia extending outwards at a complement of a 37-degree angle(143-degree angle) from the shaft or connector leg 141. In oneparticular embodiment, the EEG sensor or electrode have prongs extendingless than 2 centimeters from a central hub at equidistant angles fromone another and forming podia extending outwards at a 143 degree anglefrom the shaft and further being formed of graphene. In someembodiments, the podia can extend outwards from the shaft at anglesranging from 135 to 150 degrees from the shaft. Each senor 140 can alsohave microfilaments 144 extending from the base of the podia (140) andcan contain individual faraday cages (146) on each podia. The individualelectrodes in the plurality of electrodes can be connected to each otherand/or to a signal processing device via a wired or wireless connection.In the case of a wireless connection, any number of wireless protocolssuch as Bluetooth, WiFi, Zibbee or others can be used to provide suchwireless connection as needed. Note, in a wireless version, each sensor140 would wirelessly couple to the central hub 142 (or other signalcollection point) without the need for a wired connection (141). FIGS.14C, 14D, and 14E, depict similar alternative embodiments to the sensorarray 145 of of FIG. 14A including sensor arrays 145C, 145D, and 145Erespectively. Sensor array 145C of FIG. 14C includes a central hub 142C,a shaft or leg member 141C, and an electrode or podia 140C. A bottomportion of each of the podia 140C can be curved to ergonomically fit auser's scalp, skull or head. Further note that the central hub 142C inthis embodiment can be a receptacle for receiving a central controller(not shown) that electronically couples to the conductors in theelectrodes 140C. FIG. 14D depicts the sensor array 145D that includes acentral hub 142D, a shaft or leg member 141D, an array of electrodes orpodia 140D, and further depicts the microfilaments 144 on each electrode140D. FIG. 14E depicts the sensor array 145E that includes a central hub142E, a shaft or leg member 141E, and an array of electrodes or podia140E. Again, a bottom potion of each of the podia 140E can be curved toergonomically fit a user's scalp, skull, or head.

Referring to FIG. 15, the electrodes 140A and 140B (similar to electrode140 of FIG. 14B) or other EEG electrodes can be incorporated into a tapedesign 150 that forms at least a pair of electrodes coupled to eachother to enable better connectivity between specific sites associatedwith known brain signaling or characteristics that can be extracted fromEEG signaling. The tape design 150 can embodied as the “Neurotape”discussed above. The tape design 150 can include an adjustment element158 (such as a hinge) enabling the user or clinician to modify theplacement or location of the separate electrodes 140A and 140B. In someembodiments, the adjustment element can also alternatively have anaccordion type feature to adjust the distance between the electrodes140A and 140B or the length of their respective legs or connectors. Thetape design 150, in some embodiments, is a pure passive device thatenables greater interconnection or connectivity between specific brainsites or even brain regions. As noted above, use of the electrode 140will further enhance the ability to provide adequate connections to theappropriate and respective sites on the scalp or skull due to thestructure and composition of the electrode 140. The microfilaments 144provide better connections to the intended or desired sites and thegraphene coating on the portions 152 of the electrodes 140A and 140B(and optionally on the Faraday cage 146) further reduce interferencefrom extraneous signals that can potentially interfere with the desiredEEG signaling. In some embodiments, an external portion or outer bodyportion 154 of the tape design 150 would be formed of non-conductivematerial. The tape design 150 can also include adhesive 156 to retainthe tape design 150 in the desired position on the scalp once put inplace. In some embodiments, biofeedback aspects of ANNA can be used inconjunction with the tape design 150 to further enhance the capabilityof improved connectivity and placement of the electrodes and ultimatelyprovide more accurate readings that can be used and fed back into ANNA'sdatabase.

In some embodiments, a system includes at least one memory and at leastone processor of a computer system communicatively coupled to the atleast one memory. The at least one processor can be configured toperform a method including methods described above.

According yet to another embodiment of the present disclosure, acomputer readable storage medium comprises computer instructions which,responsive to being executed by one or more processors, cause the one ormore processors to perform operations as described in the methods orsystems above or elsewhere herein.

As shown in FIG.16, an information processing system 101 of a system 100can be communicatively coupled with the data analysis module 170 and agroup of client or other devices, or coupled to a presentation devicefor display at any location at a terminal or server location. Accordingto this example, at least one processor 102, responsive to executinginstructions 107, performs operations to communicate with the dataanalysis module 170 via a bus architecture 208, as shown. The at leastone processor 102 is communicatively coupled with main memory 104,persistent memory 106, and a computer readable medium 120. The processor102 is communicatively coupled with an Analysis & Data Storage 115 that,according to various implementations, can maintain stored informationused by, for example, the data analysis module 170 and more generallyused by the information processing system 100. Optionally, this storedinformation can be received from the client or other devices. Forexample, this stored information can be received periodically from theclient devices and updated or processed over time in the Analysis & DataStorage 115. Additionally, according to another example, a history logcan be maintained or stored in the Analysis & Data Storage 115 of theinformation processed over time. The data analysis module 150170 and theinformation processing system 100, can use the information from thehistory log such as in the analysis process and in making decisionsrelated to determining whether data measured is considered an outlier ornot.

The computer readable medium 120, according to the present example, canbe communicatively coupled with a reader/writer device (not shown) thatis communicatively coupled via the bus architecture 208 with the atleast one processor 102. The instructions 107, which can includeinstructions, configuration parameters, and data, may be stored in thecomputer readable medium 120, the main memory 104, the persistent memory106, and in the processor's internal memory such as cache memory andregisters, as shown.

In some embodiments, blocks of data are stored in memory subsystems(102, 106, 120, 117, or 108 in cloud based systems) using a blockchainor similar cryptographic hash technology to detect unauthorizedmodification or corruption of records. Thus, only authorized users suchas a patient's physician is able to see the data using a cryptographickey associated with the patient's physician. Moreover, the data can beanonymized so that the identity associated with a subject is anonymousunless the subject gives permission or authorization for thisinformation to be released. In some embodiments, an identifier for oneor more blocks in the blockchain includes a private encryption key.

Blockchain technology is widely known as the technology behind thepopular cryptocurrency, Bitcoin. A blockchain creates a history of datadeposits, messages, or transactions in a series of blocks where eachblock contains a mathematical summary, called a hash, of the previousblock. This creates a chain where any changes made to a block willchange that block's hash, which must be recomputed and stored in thenext block. This changes the hash of the next block, which must also berecomputed and so on until the end of the chain. The information beingtransmitted can be encrypted or stored to be only accessible or readableto the user himself or herself, or to someone with appropriate securityinformation. The privacy of the user, or the user's identity, can alsobe secured and maintained cryptographically. These encryption steps maybe performed locally or on a remote device. These encryption steps maybe performed on a remote server or on the cloud.

The security of a blockchain is further increased by implementing it ona distributed network. This means a large number of users all haveaccess to the blockchain and are all attempting to add blocks to the endof the chain by finding a nonce that produces a valid hash for a givenblock of data. When two blocks are found that both claim to referencethe same previous block, a fork in the chain is created. Some users inthe network will attempt to find the next block on one end of the forkwhile other users will work from the other end of the fork. Eventuallyone of the forks will surpass the other in length, and the longest chainis accepted by consensus as the valid chain. Therefore, anyone whoattempts to change a block must not only re-find a valid hash for eachsubsequent block, but must do it faster than everyone else working onthe currently accepted chain. Thus, after a certain number of blockshave been chained onto a particular block, it becomes prohibitivelycostly to try to change that block.

The information processing system 100 includes a user interface 110 thatcomprises a user output interface 112 and user input interface 114.Examples of elements of the user output interface 112 can include adisplay, a speaker, one or more indicator lights, one or moretransducers that generate audible indicators, and a haptic signalgenerator. Examples of elements of the user input interface 114 caninclude a keyboard, a keypad, a mouse, a track pad, a touch pad, amicrophone that receives audio signals, a camera, a video camera, or ascanner that scans images. The received audio signals or scanned images,for example, can be converted to electronic digital representation andstored in memory, and optionally can be used with corresponding voice orimage recognition software executed by the processor 102 to receive userinput data and commands, or to receive test data for example.

A network interface device 116 is communicatively coupled with the atleast one processor 102 and provides a communication interface for theinformation processing system 100 to communicate via one or morenetworks 108. The networks 108 can include wired and wireless networks,and can be any of local area networks, wide area networks, or acombination of such networks. For example, wide area networks includingthe internet and the web can inter-communicate the informationprocessing system 100 with other one or more information processingsystems that may be locally, or remotely, located relative to theinformation processing system 100. It should be noted that mobilecommunications devices, such as mobile phones, Smart phones, tabletcomputers, lap top computers, and the like, which are capable of atleast one of wired and/or wireless communication, are also examples ofinformation processing systems within the scope of the presentdisclosure. The network interface device 116 can provide a communicationinterface for the information processing system 100 to access the atleast one database 117 according to various embodiments of thedisclosure.

The instructions 107, according to the present example, can includeinstructions for monitoring, instructions for analyzing, instructionsfor retrieving and sending information and related configurationparameters and data. It should be noted that any portion of theinstructions 107 can be stored in a centralized information processingsystem or can be stored in a distributed information processing system,i.e., with portions of the system distributed and communicativelycoupled together over one or more communication links or networks.

FIGS. 1 and 2 illustrate examples of methods, according to variousembodiments of the present disclosure, which can operate in conjunctionwith the information processing system of FIG. 16.

Referring to FIGS. 17A, 17B, and 18, portions or constituent parts of anoverall system for ANNA are detail further in block diagrams or flowcharts. One overall exemplary block diagram is further illustrated inFIG. 19. With respect to the block diagram 200 of FIG. 17A, a machinelearning portion of ANNA can capture raw data in European Data Format(EDF) at block 202 and forward such data for conversion or processing,using, for example, MAT LAB at block 203. MAT LAB formatting andprocessing will allow easier processing for normalization and conversionto power and connectivity data at block 204. Subsequently, the power andconnectivity data can be clustered as desired at block 205. In oneexample, the clustering can be done over predetermined frequency bandsknown to provide insights into certain brain-related conditions. Withfurther respect to FIG. 17B, block diagram 201 illustrates a portion ofANNA that can perform subjective rules processing by processing oranalyzing at block 207 the power and connectivity data 204 along withsubjective rules from a block 206 to provide subjective conclusions atblock 208. Some of the subjective rules could be applied as part of aprofessional's assessment of a patient's condition at the time of theEDF data readings from the patient. For example, if the patient wasinattentive, sleepy, fidgety, or in some other subjectively noticeablestate, a subjective conclusion can be extracted as part of the process.In some embodiments, the Subjective Rules can be based on a visual oraudio recording or assessment without the use of a live professionalassessment. In either case, ANNA can be configured to subsequently allowfor corrective assessments as previously discussed in another phase ofthe system.

FIG. 18 illustrates another block diagram 300 depicting a portion ofANNA's system dealing with subjective data and sessions. Block 303collects and performs a subjective comparison analysis of subjectivedata from block 301 and from sessions 302. Subjective data can include apatient's self assessments about moods or feelings or other previousassessments and analyzed data can further include data from interactivesessions that probes the patient further regarding their condition. As aresult of the subjective comparison analysis 303, the system providessubjective conclusions based on the sessions at block 304.

FIG. 19 illustrates a block diagram of a system 400 that incorporates anumber of the previously disclosed system portions into an overallsystem in accordance with an embodiment of the disclosure. In thisembodiment, raw EDF data from block 402 provides data for conversion orprocessing into MAT LAB format at block 404 which is further normalizedor processed for connectivity at block 406 to further enable comparisonanalysis at block 410 with subjective type data from block 408. Block416 collects and performs a subjective comparison analysis of subjectivedata from block 412 and from sessions 414. As a result of the subjectivecomparison analysis or other processing at block 416 and the comparisonanalysis of block 410, the system provides results to block 418 whereusers or experts will be asked to provide verification and/or correctiveactions. The output of block 418 will be further used for building andmodifying a mapping tree of symptoms and results. Additionally, ANNAincludes a learned database 420 that receives inputs from the comparisonanalysis block 410 and result and mapping tree block 422.

The learned database 420 provides inputs to all or some of thealternative clustering methods that can be used as part of the overallsystem to provide predictions of symptoms as a result of clustering(using broad prediction analysis at block 428, or guided predictionanalysis at block 430, or narrowed prediction analysis at block 432) oras a result of flat prediction analysis at block 426. Furthermore, thesystem can incorporate a feedback system that provides the results fromany of the prediction analysis techniques (426, 428, 430, or 432) backto the result and mapping tree block 422 for continual refinement andimprovement. The blocks 428, 430, and 432 can used global clustering aspart of their analysis to cluster results and enable a modified resultbased on clustering and comparison of data already stored (regardingpossible symptoms or other pertinent data) in a database such as theANNA database or learned database 420. Further note that the system canalso cluster gathered patient data over different frequency bands atblock 424 before further prediction analysis using clustering techniquesat blocks 428, 430 or 430 or without additional clustering with a flatprediction at block 426. As an integrated system, system 400 can usemachine learning, artificial intelligence and other techniques tocontinually improve and provide the next generation of psychiatricanalysis and medicine as a tool for clinics, hospitals, individuallicensed practitioners, and other users with appropriate guidance andinstruction or supervision.

FIG. 20 illustrates a wave band cluster 502 for a system 500 havingvarious clusters 504, 506, 508, and 510. Each of these clusters mightrepresent clusters for particular frequency bands (e.g., Gamma, Beta,Low Beta, Midrange Beta, High Beta, Alpha, etc.) for EEG signalscaptured at particular sites on the skull or correlated with particularlocations of the brain (frontal lobes, occipital lobes, temporal lobes,central strip, parietal lobes, etc.). These clustered signals canprovide some predictive indications, but further clustering of thesevarious clusters (504-510) during global clustering can also provideadditional predictive indications of symptoms as discussed above.

NON-LIMITING EXAMPLES

The examples provide herein may be a system, a method, and/or a computerprogram product at any possible technical detail level of integration.The computer program product may include a computer readable storagemedium (or media) having computer readable program instructions thereonfor causing a processor to carry out aspects of the present disclosure.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present disclosure may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, configuration data for integrated circuitry, oreither source code or object code written in any combination of one ormore programming languages, including an object oriented programminglanguage such as Smalltalk, C++, or the like, and procedural programminglanguages, such as the “C” programming language or similar programminglanguages. The computer readable program instructions may executeentirely on the user's computer, partly on the user's computer, as astand-alone software package, partly on the user's computer and partlyon a remote computer or entirely on the remote computer or server. Inthe latter scenario, the remote computer may be connected to the user'scomputer through any type of network, including a local area network(LAN) or a wide area network (WAN), or the connection may be made to anexternal computer (for example, through the Internet using an InternetService Provider). In some embodiments, electronic circuitry including,for example, programmable logic circuitry, field-programmable gatearrays (FPGA), or programmable logic arrays (PLA) may execute thecomputer readable program instructions by utilizing state information ofthe computer readable program instructions to personalize the electroniccircuitry, in order to perform aspects of the present disclosure.

Aspects of the present disclosure are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of thedisclosure. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present disclosure. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the blocks may occur out of theorder noted in the Figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

Although the present specification may describe components and functionsimplemented in the embodiments with reference to particular standardsand protocols, the disclosure is not limited to such standards andprotocols. Each of the standards represents examples of the state of theart. Such standards are from time-to-time superseded by faster or moreefficient equivalents having essentially the same functions.

The illustrations of examples described herein are intended to provide ageneral understanding of the structure of various embodiments, and theyare not intended to serve as a complete description of all the elementsand features of apparatus and systems that might make use of thestructures described herein. Many other embodiments will be apparent tothose of skill in the art upon reviewing the above description. Otherembodiments may be utilized and derived therefrom, such that structuraland logical substitutions and changes may be made without departing fromthe scope of this disclosure. Figures are also merely representationaland may not be drawn to scale. Certain proportions thereof may beexaggerated, while others may be minimized. Accordingly, thespecification and drawings are to be regarded in an illustrative ratherthan a restrictive sense.

Although specific embodiments have been illustrated and describedherein, it should be appreciated that any arrangement calculated toachieve the same purpose may be substituted for the specific embodimentsshown. The examples herein are intended to cover any and all adaptationsor variations of various embodiments. Combinations of the aboveembodiments, and other embodiments not specifically described herein,are contemplated herein.

The Abstract is provided with the understanding that it is not intendedbe used to interpret or limit the scope or meaning of the claims. Inaddition, in the foregoing Detailed Description, various features aregrouped together in a single example embodiment for the purpose ofstreamlining the disclosure. This method of disclosure is not to beinterpreted as reflecting an intention that the claimed embodimentsrequire more features than are expressly recited in each claim. Rather,as the following claims reflect, inventive subject matter lies in lessthan all features of a single disclosed embodiment. Thus the followingclaims are hereby incorporated into the Detailed Description, with eachclaim standing on its own as a separately claimed subj ect matter.

Although only one processor is illustrated for an information processingsystem, information processing systems with multiple CPUs or processorscan be used equally effectively. Various embodiments of the presentdisclosure can further incorporate interfaces that each includesseparate, fully programmed microprocessors that are used to off-loadprocessing from the processor. An operating system (not shown) includedin main memory for the information processing system may be a suitablemultitasking and/or multiprocessing operating system, such as, but notlimited to, any of the Linux, UNIX, Windows, and Windows Server basedoperating systems. Various embodiments of the present disclosure areable to use any other suitable operating system. Various embodiments ofthe present disclosure utilize architectures, such as an object orientedframework mechanism, that allows instructions of the components ofoperating system (not shown) to be executed on any processor locatedwithin the information processing system. Various embodiments of thepresent disclosure are able to be adapted to work with any datacommunications connections including present day analog and/or digitaltechniques or via a future networking mechanism.

The terminology used herein is for the purpose of describing particularembodiments only and is not intended to be limiting of the disclosure.As used herein, the singular forms “a”, “an” and “the” are intended toinclude the plural forms as well, unless the context clearly indicatesotherwise. It will be further understood that the terms “comprises”and/or “comprising,” when used in this specification, specify thepresence of stated features, integers, steps, operations, elements,and/or components, but do not preclude the presence or addition of oneor more other features, integers, steps, operations, elements,components, and/or groups thereof. The term “another”, as used herein,is defined as at least a second or more. The terms “including” and“having,” as used herein, are defined as comprising (i.e., openlanguage). The term “coupled,” as used herein, is defined as“connected,” although not necessarily directly, and not necessarilymechanically. “Communicatively coupled” refers to coupling of componentssuch that these components are able to communicate with one anotherthrough, for example, wired, wireless or other communications media. Theterms “communicatively coupled” or “communicatively coupling” include,but are not limited to, communicating electronic control signals bywhich one element may direct or control another. The term “configuredto” describes hardware, software or a combination of hardware andsoftware that is adapted to, set up, arranged, built, composed,constructed, designed or that has any combination of thesecharacteristics to carry out a given function. The term “adapted to”describes hardware, software or a combination of hardware and softwarethat is capable of, able to accommodate, to make, or that is suitable tocarry out a given function.

The terms “controller”, “computer”, “processor”, “server”, “client”,“computer system”, “computing system”, “personal computing system”,“processing system”, or “information processing system”, describeexamples of a suitably configured processing system adapted to implementone or more embodiments herein. Any suitably configured processingsystem is similarly able to be used by embodiments herein, for exampleand not for limitation, a personal computer, a laptop personal computer(laptop PC), a tablet computer, a smart phone, a mobile phone, awireless communication device, a personal digital assistant, aworkstation, and the like. A processing system may include one or moreprocessing systems or processors. A processing system can be realized ina centralized fashion in one processing system or in a distributedfashion where different elements are spread across severalinterconnected processing systems.

The corresponding structures, materials, acts, and equivalents of allmeans or step plus function elements in the claims below are intended toinclude any structure, material, or act for performing the function incombination with other claimed elements as specifically claimed. Thedescription herein has been presented for purposes of illustration anddescription, but is not intended to be exhaustive or limited to theexamples in the form disclosed. Many modifications and variations willbe apparent to those of ordinary skill in the art without departing fromthe scope of the examples presented or claimed. The disclosedembodiments were chosen and described in order to explain the principlesof the embodiments and the practical application, and to enable othersof ordinary skill in the art to understand the various embodiments withvarious modifications as are suited to the particular use contemplated.It is intended that the appended claims below cover any and all suchapplications, modifications, and variations within the scope of theembodiments.

What is claimed is:
 1. A system for analyzing electroencephalogramsignals, comprising: a plurality of sensors configured to contact askull and capture the electroencephalogram signals; one or more computermemory units for storing computer instructions and data; and one or moreprocessors operatively coupled to the one or more computer memory unitsand the plurality of sensors, the one or more processors configured toperform the operations of: clustering the electroencephalogram signalsusing at least stored objective data and added subjective data includingpatient profile data to provide clustered data results; and predictingone or more among a medical diagnosis, assessment, plan, necessaryforms, or recommendations for follow up based on the clustered dataresults.
 2. The system of claim 1, wherein the one or more processorsare configured to perform data transformation of theelectroencephalogram signals to enable a comparison of normalizedelectroencephalogram signals with the stored objective and the addedsubjective data.
 3. The system of claim 2, wherein the datatransformation is done by using one or more of the analysis methodscomprising absolute power analysis, relative power analysis, amplitudeasymmetry connectivity analysis, coherence connectivity analysis, phaselag analysis, phase shift analysis, phase lock analysis, current sourcedensity analysis, evoked potential analysis, or low resolutionelectrographic analysis.
 4. The system of claim 2, wherein the datatransformation is done by using absolute power analysis by performing aFast Fourier Transform between the electroencephalogram signals and acomplex Morlet wavelet sine wave for each frequency range of theelectroencephalogram signals captured and comparing the Fast FourierTransform to a derived normal based on a Z-score Gaussian distribution.5. The system of claim 1, wherein one or more blocks in a blockchain inthe one or more memory units includes a private encryption key.
 6. Thesystem of claim 1, wherein the one or more processors clusters theelectroencephalogram signals over different frequent bands and furtherclusters the electroencephalogram signals with cloud based storedpatterns in a database for analysis and prediction.
 7. The system ofclaim 1, wherein the one or more processors presents options forpredicting the diagnosis using a selection among an unguided prediction,a guided prediction, a narrowed down prediction, or a flat prediction.8. The system of claim 1, wherein the one or more processors presents aguided prediction with weighting applied to matching patterns amongclustered results.
 9. The system of claim 1, wherein the one or moreprocessors presents an option enabling an expert to correct or verifythe diagnosis predicted by the system.
 10. The system of claim 1,wherein the one or more processors maps and stores the diagnosispredicted by the system by mapping a patient's symptom with theelectroencephalogram signals in a database.
 11. The system of claim 1,wherein the plurality of sensors are electroencephalogram sensors havingprongs extending less than two centimeters from a central hub atequidistant angles from one another and forming podia extending outwardsat an angle from the shaft and further formed of graphene and whereineach senor has a microfilament extending from the base of the podia andcontains individual faraday cages on each podia.
 12. A computerimplemented method of analyzing electroencephalogram signals,comprising: capturing the electroencephalogram signals using a pluralityof sensors configured to contact a skull; clustering, by one or moreprocessors, the electroencephalogram signals using at least storedobjective data and subjective data including patient profile data toprovide clustered data results; and predicting, by the one or moreprocessors, one or more among a medical plan based on current practiceparameters for the field of psychiatry or a diagnosis based on theclustered data results.
 13. The method of claim 12, wherein the one ormore processors are configured to perform data transformation of theelectroencephalogram signals to enable a comparison of normalizedelectroencephalogram signals with the stored objective data.
 14. Themethod of claim 13, wherein the data transformation is done by using oneor more of the analysis methods comprising absolute power analysis,relative power analysis, amplitude asymmetry connectivity analysis,coherence connectivity analysis, phase lag analysis, phase shiftanalysis, phase lock analysis or source density vector averaging. 15.The method of claim 12, wherein the one or more processors clusters theelectroencephalogram signals using a K-Means algorithm.
 16. The methodof claim 12, wherein the one or more processors presents options forpredicting the diagnosis using a selection among an unguided prediction,a guided prediction, a narrowed down prediction, or a flat prediction.17. The method of claim 12, wherein the one or more processors presentsan option enabling an expert to correct or verify the diagnosispredicted by the method.
 18. A non-transitory computer-readable storagemedium having stored therein instructions which, when executed by atleast one or more processors of at least one computing device, cause acomputer system to perform a method comprising: capturing theelectroencephalogram signals using a plurality of sensors configured tocontact a skull; clustering, by one or more processors, theelectroencephalogram signals using at least stored objective data andsubjective data including patient profile data to provide clustered dataresults; and predicting, by the one or more processors, a diagnosisbased on the clustered data results.
 19. The non-transitorycomputer-readable storage medium of claim 18, further comprisinginstructions causing the computer system to perform data prediction fora best electroencephalogram biofeedback algorithm based on the storedobjective data and the subjective data.
 20. The non-transitorycomputer-readable storage medium of claim 18, further comprisinginstructions causing the computer system to present an option enablingan expert to correct or verify the diagnosis predicted by the method.